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What goes into a monthly estimate?
Use this formula, adding other operating expenses only when you have a real figure for them:
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Total estimated monthly cost = LLM usage cost + WhatsApp delivery cost + provider/platform fees + other explicitly supplied operating costs.
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Keep each component separate. That makes it clear whether a change in your estimate comes from model usage, WhatsApp delivery, or a partner’s charges.
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How do you estimate LLM usage?
For each billed token category, multiply your expected monthly tokens by that category’s rate per million, then divide by 1,000,000. Add the category subtotals:
LLM cost = (input tokens ÷ 1,000,000 × input rate) + (cached-input tokens ÷ 1,000,000 × cached-input rate) + (output tokens ÷ 1,000,000 × output rate).
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This is the formula given on OpenAI’s Enterprise token-based rate card. Its rates apply to eligible token-based Enterprise agreements, and applicable terms may differ by agreement; it is not a universal API price list.
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As one dated example, the rate card lists GPT-5.6 Sol at $4.00 per million input tokens, $0.40 per million cached-input tokens, and $20.00 per million output tokens. These are OpenAI Enterprise rate-card values, not a general API tariff. To calculate your own cost, enter your account’s applicable rates and your expected volumes in each billed category. If a category does not apply to your model or billing arrangement, do not include it.
API usage and ChatGPT seat subscriptions are separate commercial products. OpenAI’s business pricing page lists ChatGPT API separately from seat plans. A seat price is not a substitute for API token usage.
How do you estimate WhatsApp delivery charges?
For each relevant recipient market and message category, multiply the delivered volume by the applicable per-message rate, then add the subtotals. The market is based on the recipient’s country calling code. Do not apply one country’s or category’s rate to all messages.
Meta says API fees follow its Rate Card and pricing rules, and that it may update the Rate Card. Its WhatsApp pricing documentation is the place to check applicable current rates. No exact per-message amounts are included here: the relevant figures must be checked against the current official card for the recipient market and category, then dated in your estimate. Confirm the current rules for which messages count as delivered and billable before projecting volume.
Where do platform and partner fees belong?
Record a solution provider’s charges on a separate line from Meta message fees. A provider may charge a markup, a fixed monthly fee, or another amount under its own terms; it is not automatically a Meta charge. Meta’s pricing reference says charges between an end customer and a Solution Partner are determined between those parties. Check your provider’s terms for the country you plan to serve.
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Use a short worksheet or spreadsheet with one row per distinct rate or usage category. Enter the source and effective date beside each rate, rather than keeping rates in an unlabeled total.
- LLM: provider and model; rate effective date; expected monthly input, cached-input, and output tokens where applicable; and your own rate per million for each billed category.
- WhatsApp: recipient country or market; message category; expected monthly delivered volume; and the rate from the applicable current Meta card.
- Provider/platform: any markup, fixed monthly fee, or other documented charge, kept distinct from Meta fees.
- Other operating costs: add only amounts you have actually supplied, such as a known infrastructure expense.
For each line, calculate volume × rate using consistent units. Token rates stated per million require dividing the token count by one million first. Sum the line items only after those calculations. Keep currencies visible and convert only if you have a stated exchange-rate assumption.
If you do not yet know usage, create low, base, and high scenarios using your own anticipated volumes. Label them as scenarios, not market averages. There is no generally representative usage distribution established here, so a scenario should reflect your workflow: how often users contact it, how long exchanges run, and how much text the model processes and returns.
Which assumptions are most useful to vary?
A sensitivity check shows what could move the total most. Change one assumption at a time, then test combinations that match plausible changes in your workflow.
Quick Recap
- Model and token mix: compare the rate you actually qualify for and vary input versus output volume; include cached input only if it applies to your billing.
- Market and category: recalculate WhatsApp delivery using the relevant recipient market and message category rather than a blended universal rate.
- Message volume: vary delivered volume while keeping the rate and category assumptions visible.
- Provider charges: test the effect of a markup or fixed fee separately, so partner charges do not disappear inside the delivery estimate.
What can make an estimate misleading?
- Using a seat subscription as an API rate: they are separate products, so use the applicable API provider pricing for token usage.
- Treating an Enterprise rate card as universal: the cited OpenAI values are agreement-scoped; check the terms and rates that apply to your own account.
- Using one WhatsApp price everywhere: Meta pricing depends on market and category, and its Rate Card may change.
- Combining partner fees with Meta fees: keep the provider’s commercial terms distinct so you can identify each cost source.
- Presenting a scenario as a typical bill: show the usage and rate assumptions beside every calculated total, and date volatile rates.
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